Ke Zhang1,2, Yiyue Ren3,4, Shufeng Xu2,5, Wei Lu2,6, Shengnan Xie2, Jiali Qu2, Xiaoyan Wang2, Bo Shen2,7, Peipei Pang8, Xiujun Cai3,4, Jihong Sun1,2. 1. Shaoxing University School of Medicine, Shaoxing, China. 2. Department of Radiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. 3. Department of General Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China. 4. Key Laboratory of Endoscopic Technique Research of Zhejiang Province, Zhejiang University. 5. Department of Radiology, People's Hospital of Quzhou, Quzhou Hospital affiliated to Wenzhou Medical University, Quzhou, China. 6. Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, China. 7. Department of Radiology, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University; Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, China. 8. Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China.
Abstract
PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high-throughput radiomics features and clinical factors for accurate prediction of LVI/PNI in these patients. METHODS: Two hundred and sixty-three patients who underwent colorectal resection for histologically confirmed CRC between 1 February, 2011 and 30 June, 2020 were retrospectively enrolled. Between 1 February, 2011 and 30 September, 2018, 213 patients were randomly divided into a training cohort (n=149) and a validation cohort (n=64) by a ratio of 7:3. We used a 10000-iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October, 2018 and 30 June, 2020. Regions of interest (ROIs) were manually delineated in high-resolution T2-weighted and diffusion-weighted images using ITK-SNAP software on each CRC tumour slice. In total, 3356 radiomics features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical-radiomics model for predicting LVI/PNI. Receiver-operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation and independent test cohorts. RESULTS: A multi-parametric clinical-radiomics model combining MRI-reported extramural vascular invasion (EMVI) status and a Rad-score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85-0.97), validation cohort (AUC: 0.88; 95% CI: 0.79-89) and independent test cohorts (AUC 0.83, 95% CI 0.72-0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. CONCLUSION: Multiparametric clinical-radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualised treatment decisions. This article is protected by copyright. All rights reserved.
PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are independent prognostic factors in patients with colorectal cancer (CRC). In this study, we aimed to develop and validate a preoperative predictive model based on high-throughput radiomics features and clinical factors for accurate prediction of LVI/PNI in these patients. METHODS: Two hundred and sixty-three patients who underwent colorectal resection for histologically confirmed CRC between 1 February, 2011 and 30 June, 2020 were retrospectively enrolled. Between 1 February, 2011 and 30 September, 2018, 213 patients were randomly divided into a training cohort (n=149) and a validation cohort (n=64) by a ratio of 7:3. We used a 10000-iteration bootstrap analysis to estimate the prediction error and confidence interval for two cohorts. The independent test cohort consisted of 50 patients between 1 October, 2018 and 30 June, 2020. Regions of interest (ROIs) were manually delineated in high-resolution T2-weighted and diffusion-weighted images using ITK-SNAP software on each CRC tumour slice. In total, 3356 radiomics features were extracted from each ROI. Next, we used the maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms to select the strongest of these features to establish a clinical-radiomics model for predicting LVI/PNI. Receiver-operating characteristic and calibration curves were then plotted to evaluate the predictive performance of the model in the training, validation and independent test cohorts. RESULTS: A multi-parametric clinical-radiomics model combining MRI-reported extramural vascular invasion (EMVI) status and a Rad-score for the LVI/PNI estimation was established. This model had significant predictive power in the training cohort (area under the curve [AUC] 0.91; 95% confidence interval [CI]: 0.85-0.97), validation cohort (AUC: 0.88; 95% CI: 0.79-89) and independent test cohorts (AUC 0.83, 95% CI 0.72-0.95). The model performed well in the independent test cohort with sensitivity of 0.818, specificity of 0.714, and accuracy of 0.760. Calibration curve and decision curve analysis demonstrated clinical benefits. CONCLUSION: Multiparametric clinical-radiomics models can accurately predict LVI/PNI in patients with CRC. Our model has predictive ability that should improve preoperative diagnostic performance and allow more individualised treatment decisions. This article is protected by copyright. All rights reserved.